糖心vlog官网观看

Chevron Left
Back to Supervised Machine Learning: Regression and Classification

Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
30,880 ratings

About the Course

In the first course of the Machine Learning Specialization, you will: 鈥� Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. 鈥� Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew鈥檚 pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you鈥檙e looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Top reviews

MA

Jan 28, 2025

I've really enjoyed learning about Machine Learning in such a guided way. It will continue to inspire me to learn more about AI. Thank you Andrew Ng, DeepLearning.AI, Standford ONLINE, and 糖心vlog官网观看.

AA

Apr 30, 2023

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.

Filter by:

4051 - 4075 of 5,849 Reviews for Supervised Machine Learning: Regression and Classification

By Justin D

鈥�

Aug 12, 2025

great course for beginners

By J L

鈥�

Aug 10, 2025

Great learning experience!

By Abdullah R

鈥�

Jul 28, 2025

very fun practice for test

By Hamza B

鈥�

Jul 9, 2025

great course i learnt alot

By Leah H

鈥�

May 9, 2025

Andrew is a lovely person.

By Kabilan K

鈥�

May 8, 2025

This was an amazing course

By Farrel H

鈥�

Mar 20, 2025

Having a great experience!

By Fatih A

鈥�

Jan 12, 2025

It was an excellent course

By Fakhrutdinov A

鈥�

Jan 4, 2025

most interesting ML course

By Jiang X

鈥�

Jan 1, 2025

Easy to understand and use

By Avishek C

鈥�

Nov 26, 2024

Phenomenal course it is...

By BIKRAM S

鈥�

Nov 10, 2024

IT IS BEST FOR NOOBS IN ML

By Arafat H

鈥�

Oct 3, 2024

Very helpful course indeed

By VISHAL

鈥�

Sep 14, 2024

it was a great experience.

By Federico

鈥�

Apr 3, 2024

Great introductory course!

By Johan S

鈥�

Jan 21, 2024

Great course for beginners

By Jonathan M V

鈥�

Dec 27, 2023

Great teacher. Great Labs.

By Lingxuan Z

鈥�

Dec 26, 2023

Really good course, bravo!

By Francesco P

鈥�

Sep 29, 2023

Bestl course for beginners

By katarina j

鈥�

Sep 26, 2023

A big thank you to Andrew.

By Steffen B

鈥�

Sep 22, 2023

Thanks Andrew, great work!

By Brett H

鈥�

Aug 10, 2023

Just excellent, that's all

By Navya N

鈥�

Aug 1, 2023

great course helped a lot

By Mtir M

鈥�

Jul 16, 2023

Parfait , Merci beaucoup !

By Satya M P

鈥�

Jun 27, 2023

Love it. Awesome teaching.